Communities' Detection in Social Networks: State of the art and perspectives

2018 ◽  
Author(s):  
Rachid Djerbi ◽  
Rabah Imache ◽  
Mourad Amad
Keyword(s):  
Social Networks ◽  
State Of The Art

On the Privacy and Utility of Anonymized Social Networks

2013 ◽  
Vol 4 (2) ◽  
pp. 1-34 ◽  
Author(s):  
Yi Song ◽  
Xuesong Lu ◽  
Sadegh Nobari ◽  
Stéphane Bressan ◽  
Panagiotis Karras
Keyword(s):  
Social Networks ◽  
Social Media ◽  
State Of The Art ◽  
Social Groups ◽  
Trade Off ◽  
Private Lives ◽  
The Social ◽  
Before And After ◽  
Do So

One is either on Facebook or not. Of course, this assessment is controversial and its rationale arguable. It is nevertheless not far, for many, from the reason behind joining social media and publishing and sharing details of their professional and private lives. Not only the personal details that may be revealed, but also the structure of the networks are sources of invaluable information for any organization wanting to understand and learn about social groups, their dynamics and members. These organizations may or may not be benevolent. It is important to devise, design and evaluate solutions that guarantee some privacy. One approach that reconciles the different stakeholders’ requirement is the publication of a modified graph. The perturbation is hoped to be sufficient to protect members’ privacy while it maintains sufficient utility for analysts wanting to study the social media as a whole. In this paper, the authors try to empirically quantify the inevitable trade-off between utility and privacy. They do so for two state-of-the-art graph anonymization algorithms that protect against most structural attacks, the k-automorphism algorithm and the k-degree anonymity algorithm. The authors measure several metrics for a series of real graphs from various social media before and after their anonymization under various settings.

Modeling Rumors in Twitter

2016 ◽  
Vol 3 (4) ◽  
pp. 46-67 ◽  
Author(s):  
Rhythm Walia ◽  
M.P.S. Bhatia
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Comparative Analysis ◽  
State Of The Art ◽  
Viral Marketing ◽  
Daily Lives ◽  
The Past ◽  
Depth Analysis ◽  
Analysis Of Results ◽  
Effective Channel

With the advent of web 2.0 and anonymous free Internet services available to almost everyone, social media has gained immense popularity in disseminating information. It has become an effective channel for advertising and viral marketing. People rely on social networks for news, communication and it has become an integral part of our daily lives. But due to the limited accountability of users, it is often misused for the spread of rumors. Such rumor diffusion hampers the credibility of social media and may spread social panic. Analyzing rumors in social media has gained immense attention from the researchers in the past decade. In this paper the authors provide a survey of work in rumor analysis, which will serve as a stepping-stone for new researchers. They organized the study of rumors into four categories and discussed state of the art papers in each with an in-depth analysis of results of different models used and a comparative analysis between approaches used by different authors.

scholarly journals Beyond the Core: Who Has Larger Social Networks?

Social Forces ◽  
2020 ◽  
Author(s):  
Bas Hofstra ◽  
Rense Corten ◽  
Frank van Tubergen
Keyword(s):  
Social Networks ◽  
Individual Variation ◽  
Bayesian Modeling ◽  
State Of The Art ◽  
Scale Up ◽  
Network Size ◽  
Future Research ◽  
Social Contacts ◽  
Core Networks ◽  
Social Network Size

Abstract The sociological literature on social networks overwhelmingly considers the number of core social contacts. Social networks, however, reach far beyond this small number of social ties. We know little about individual variation in the size of such extended social networks. In this study, we move beyond core networks and explain individual variation in the extended social network size among youth. We use survey data of Dutch adolescents (N = 5,921) and use two state-of-the-art measurements to compute extended network sizes: network scale-up methods through Bayesian modeling and the observed number of contacts on Facebook. Among both measurements, we find that extended networks are larger among ethnic majority members, girls, and those who often engage in social foci. This highlights a crucial role for preferences and opportunity in the genesis of extended networks. Additionally, we find that differences between both network sizes (scale-up and Facebook) are smaller for girls and higher educated. We discuss the implications of these findings and suggest directions for future research.

scholarly journals Link prediction in social networks: the state-of-the-art

2014 ◽  
Vol 58 (1) ◽  
pp. 1-38 ◽  
Author(s):  
Peng Wang ◽  
BaoWen Xu ◽  
YuRong Wu ◽  
XiaoYu Zhou
Keyword(s):  
Social Networks ◽  
Link Prediction ◽  
State Of The Art ◽  
The State

Community detection in large-scale social networks: state-of-the-art and future directions

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Mehdi Azaouzi ◽  
Delel Rhouma ◽  
Lotfi Ben Romdhane
Keyword(s):  
Social Networks ◽  
Community Detection ◽  
Large Scale ◽  
State Of The Art ◽  
Future Directions

Incorporating Trust Relation with PMF to Enhance Social Network Recommendation Performance

2016 ◽  
Vol 30 (06) ◽  
pp. 1659016 ◽  
Author(s):  
Hui Li ◽  
Xiao-Ping Ma ◽  
Jun Shi
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Matrix Factorization ◽  
Web Applications ◽  
State Of The Art ◽  
Large Datasets ◽  
Trust Relation ◽  
The Empirical Analysis

In view of the exponential growth of information generated by social networks, social network analysis and recommendation have become important for many web applications. This paper examines the problem of social collaborative filtering to recommend items of interest to users in a social network setting. Many social networks capture the relationships among the nodes by using trust scores to label the edges. The bias of a node denotes its propensity to trust/mistrust its neighbors and is closely related to truthfulness. It is based on the idea that the recommendation of a highly biased node should be removed. In this paper, we propose a model-based approach for recommendation employing matrix factorization after removing the bias nodes from each link, which naturally fuses the users’ tastes and their trusted friends’ favors together. The empirical analysis on real large datasets demonstrate that our approaches outperform other state-of-the-art methods.

scholarly journals Co-authorship prediction in academic social network

2019 ◽  
Author(s):  
William Takahiro Maruyama ◽  
Luciano Antonio Digiampietri
Keyword(s):  
Artificial Intelligence ◽  
Social Networks ◽  
Social Network ◽  
State Of The Art ◽  
The State ◽  
Artificial Intelligence Techniques ◽  
Academic Social Network ◽  
Academic Social Networks

The prediction of relationships in a social network is a complex and extremely useful task to enhance or maximize collaborations by indicating the most promising partnerships. In academic social networks, prediction of relationships is typically used to try to identify potential partners in the development of a project and/or co-authors for publishing papers. This paper presents an approach to predict coauthorships combining artificial intelligence techniques with the state-of-the-art metrics for link predicting in social networks.

scholarly journals Finding Central Vertices and Community Structure via Extended Density Peaks-Based Clustering

Information ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 501
Author(s):  
Yuanyuan Meng ◽  
Xiyu Liu
Keyword(s):  
Social Networks ◽  
Community Structure ◽  
State Of The Art ◽  
Research Field ◽  
Community Centers ◽  
Density Peaks ◽  
Resolution Limits ◽  
Significant Research ◽  
Connection Coefficient ◽  
Real Community

Community detection is a significant research field of social networks, and modularity is a common method to measure the division of communities in social networks. Many classical algorithms obtain community partition by improving the modularity of the whole network. However, there is still a challenge in community division, which is that the traditional modularity optimization is difficult to avoid resolution limits. To a certain extent, the simple pursuit of improving modularity will cause the division to deviate from the real community structure. To overcome these defects, with the help of clustering ideas, we proposed a method to filter community centers by the relative connection coefficient between vertices, and we analyzed the community structure accordingly. We discuss how to define the relative connection coefficient between vertices, how to select the community centers, and how to divide the remaining vertices. Experiments on both real and synthetic networks demonstrated that our algorithm is effective compared with the state-of-the-art methods.

scholarly journals Truss Decomposition using Triangle Graphs

2021 ◽  
Author(s):  
Mohsen Rezvani ◽  
Mojtaba Rezvani
Keyword(s):  
Social Networks ◽  
Large Scale ◽  
State Of The Art ◽  
Decomposition Algorithms ◽  
Scalable Algorithm ◽  
Large Scale Network ◽  
Triangle Graph ◽  
Speed Up ◽  
Scale Network ◽  
High Degree

Abstract Recent studies have shown that social networks exhibit interesting characteristics such as community structures, i.e., vertexes can be clustered into communities that are densely connected together and loosely connected to other vertices. In order to identify communities, several definitions have been proposed that can characterize the density of connections among vertices in the networks. Dense triangle cores, also known as $k$-trusses, are subgraphs in which every edge participates at least $k-2$ triangles (a clique of size 3), exhibiting a high degree of cohesiveness among vertices. There are a number of research works that propose $k$-truss decomposition algorithms. However, existing in-memory algorithms for computing $k$-truss are inefficient for handling today’s massive networks. In this paper, we propose an efficient, yet scalable algorithm for finding $k$-trusses in a large-scale network. To this end, we propose a new structure, called triangle graph to speed up the process of finding the $k$-trusses and prove the correctness and efficiency of our method. We also evaluate the performance of the proposed algorithms through extensive experiments using real-world networks. The results of comprehensive experiments show that the proposed algorithms outperform the state-of-the-art methods by several orders of magnitudes in running time.

scholarly journals A Social Interaction Activity based Time-Varying User Vectorization Method for Online Social Networks

2018 ◽  
Author(s):  
Tianyi Hao ◽  
Longbo Huang
Keyword(s):  
Social Networks ◽  
Social Interaction ◽  
Online Social Networks ◽  
User Modeling ◽  
State Of The Art ◽  
Time Varying ◽  
Self Similarity ◽  
Pairwise Interactions ◽  
User Data ◽  
Model Training

In this paper, we consider the problem of user modeling in online social networks, and propose a social interaction activity based user vectorization framework, called the time-varying user vectorization (Tuv), to infer and make use of important user features. Tuv is designed based on a novel combination of word2vec, negative sampling and a smoothing technique for model training. It jointly handles multi-format user data and computes user representing vectors, by taking into consideration user feature variation, self-similarity and pairwise interactions among users. The framework enables us to extract hidden user properties and to produce user vectors. We conduct extensive experiments based on a real-world dataset, which show that Tuv significantly outperforms several state-of-the-art user vectorization methods.

Sign in / Sign up

Export Citation Format

Share Document